Signed path dependence in financial markets: Applications and implications

Fabio Dias

Feb 2021 · Ink Magic Publishing

Ebook

194

Pages

Eligible

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About this ebook

In Signed path dependence in financial markets: Applications and implications, computer scientist and academic Fabio Dias delves into cutting-edge techniques at the intersection of machine learning, time series analysis, and finance. This comprehensive guide bridges theory and application, offering readers insights into predictive modeling, algorithmic trading, and the nuanced dynamics of option pricing. Dias combines rigorous econometric methods with hands-on machine learning approaches, presenting a toolkit for anyone looking to leverage data-driven insights to navigate and predict complex financial markets. An essential read for practitioners, researchers, and students of financial engineering and quantitative finance.

About the author

Fabio Dias is a Brazilian computer scientist, entrepreneur and academic. A computing enthusiast, Dias self-taught computer programming while doing his secondary education and became a professional software developer at the age of 15. He obtained a BSc in Computer Science and a Master of Science in Statistics from the University of São Paulo.

He subsequently relocated from Brazil to the United Kingdom, joining the technology department of Credit Suisse and thereafter moving to a senior manager role at Lloyds Banking Group. He completed his PhD in Econometrics and Statistical Science at the University College London. He is the CEO and Co-Founder of Stalwart Holdings, an academically-based investment management fintech with clients in the United Kingdom and Brazil. He also teaches financial modelling and applied computer science disciplines at the University of Surrey.

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